import cosmotool as ct import numpy as np import cosmolopy as cpy from cosmogrowth import * import borgadaptor as ba def gen_posgrid(N, L): """ Generate an ordered lagrangian grid""" ix = (np.arange(N)*L/N).astype(np.float32) x = ix[:,None,None].repeat(N, axis=1).repeat(N, axis=2) y = ix[None,:,None].repeat(N, axis=0).repeat(N, axis=2) z = ix[None,None,:].repeat(N, axis=0).repeat(N, axis=1) return x.reshape((x.size,)), y.reshape((y.size,)), z.reshape((z.size,)) def bin_power(P, L, bins=20, range=(0,1.), dev=False): N = P.shape[0] ik = np.fft.fftfreq(N, d=L/N)*2*np.pi k = np.sqrt(ik[:,None,None]**2 + ik[None,:,None]**2 + ik[None,None,:(N/2+1)]**2) H,b = np.histogram(k, bins=bins, range=range) Hw,b = np.histogram(k, bins=bins, weights=P, range=range) if dev: return Hw/(H-1), 0.5*(b[1:]+b[0:bins]), 1.0/np.sqrt(H) else: return Hw/(H-1), 0.5*(b[1:]+b[0:bins]) def compute_power_from_borg(input_borg, a_borg, cosmo, bins=10, range=(0,1)): borg_vol = ct.read_borg_vol(input_borg) N = borg_vol.density.shape[0] cgrowth = CosmoGrowth(**cosmo) D1 = cgrowth.D(1) D1_0 = D1/cgrowth.D(a_borg) print("D1_0=%lg" % D1_0) density_hat, L = ba.half_pixel_shift(borg_vol) return bin_power(D1_0**2*np.abs(density_hat)**2/L**3, L, bins=bins, range=range) def compute_ref_power(L, N, cosmo, bins=10, range=(0,1), func='HU_WIGGLES'): ik = np.fft.fftfreq(N, d=L/N)*2*np.pi k = np.sqrt(ik[:,None,None]**2 + ik[None,:,None]**2 + ik[None,None,:(N/2+1)]**2) p = ct.CosmologyPower(**cosmo) p.setFunction(func) p.normalize(cosmo['SIGMA8']) return bin_power(p.compute(k)*cosmo['h']**3, L, bins=bins, range=range) def run_generation(input_borg, a_borg, a_ic, cosmo, supersample=1, do_lpt2=True, shiftPixel=False, needvel=True): """ Generate particles and velocities from a BORG snapshot. Returns a tuple of (positions,velocities,N,BoxSize,scale_factor).""" borg_vol = ct.read_borg_vol(input_borg) N = borg_vol.density.shape[0] cgrowth = CosmoGrowth(**cosmo) density, L = ba.half_pixel_shift(borg_vol, doshift=shiftPixel) lpt = LagrangianPerturbation(density, L, fourier=True, supersample=supersample) # Generate grid posq = gen_posgrid(N*supersample, L) vel= [] posx = [] # Compute LPT scaling coefficient D1 = cgrowth.D(a_ic) D1_0 = D1/cgrowth.D(a_borg) velmul = cgrowth.compute_velmul(a_ic) D2 = -3./7 * D1_0**2 for j in xrange(3): # Generate psi_j (displacement along j) print("LPT1 axis=%d" % j) psi = D1_0*lpt.lpt1(j) psi = psi.reshape((psi.size,)) if do_lpt2: print("LPT2 axis=%d" % j) psi2 = lpt.lpt2(j) psi += D2 * psi2.reshape((psi2.size,)) # Generate posx posx.append(((posq[j] + psi)%L).astype(np.float32)) # Generate vel if needvel: vel.append((psi*velmul).astype(np.float32)) print("velmul=%lg" % (cosmo['h']*velmul)) lpt.cube.dhat = lpt.dhat density = lpt.cube.irfft() density *= (cgrowth.D(1)/cgrowth.D(a_borg)) return posx,vel,density,N*supersample,L,a_ic,cosmo @ct.timeit_quiet def whitify(density, L, cosmo, supergenerate=1, func='HU_WIGGLES'): N = density.shape[0] p = ct.CosmologyPower(**cosmo) p.setFunction(func) p.normalize(cosmo['SIGMA8']) @ct.timeit_quiet def build_Pk(): ik = np.fft.fftfreq(N, d=L/N)*2*np.pi k = np.sqrt(ik[:,None,None]**2 + ik[None,:,None]**2 + ik[None,None,:(N/2+1)]**2) return p.compute(k)*cosmo['h']**3*L**3 Pk = build_Pk() Pk[0,0,0]=1 cube = CubeFT(N, L) cube.density = density density_hat = cube.rfft() density_hat /= np.sqrt(Pk) Ns = N*supergenerate density_hat_super = np.zeros((Ns,Ns,Ns/2+1), dtype=np.complex128) density_hat_super[:] = np.nan # Copy density hat in place hN = N/2 density_hat_super[:hN, :hN, :hN+1] = density_hat[:hN, :hN, :] density_hat_super[:hN, (Ns-hN):Ns, :hN+1] = density_hat[:hN, hN:, :] density_hat_super[(Ns-hN):Ns, (Ns-hN):Ns, :hN+1] = density_hat[hN:, hN:, :] density_hat_super[(Ns-hN):Ns, :hN, :hN+1] = density_hat[hN:, :hN, :] # The moved nyquist place is untouched (so loss of "noise") to keep the structure # now we just add some noise term if supergenerate > 1: cond=np.isnan(density_hat_super) print np.where(np.isnan(density_hat_super))[0].size Nz = np.count_nonzero(cond) density_hat_super.real[cond] = np.random.randn(Nz) density_hat_super.imag[cond] = np.random.randn(Nz) density_hat_super[cond] /= np.sqrt(2.0) print np.where(np.isnan(density_hat_super))[0].size # Now we have to fix the Nyquist plane hNs = Ns/2 nyquist = density_hat_super[:, :, hNs] Nplane = nyquist.size nyquist.flat[:Nplane/2] = np.sqrt(2.0)*nyquist.flat[Nplane:Nplane/2:-1].conj() print np.where(np.isnan(density_hat_super))[0].size cube = CubeFT(Ns, L) cube.dhat = density_hat_super return np.fft.irfftn(density_hat_super)*Ns**1.5 def write_icfiles(*generated_ic, **kwargs): """Write the initial conditions from the tuple returned by run_generation""" supergenerate=1 if 'supergenerate' in kwargs: supergenerate=kwargs['supergenerate'] posx,vel,density,N,L,a_ic,cosmo = generated_ic ct.simpleWriteGadget("borg.gad", posx, velocities=vel, boxsize=L, Hubble=cosmo['h'], Omega_M=cosmo['omega_M_0'], time=a_ic) for i,c in enumerate(["x","y","z"]): ct.writeGrafic("ic_velc%s" % c, vel[i].reshape((N,N,N)), L, a_ic, **cosmo) ct.writeGrafic("ic_deltab", density, L, a_ic, **cosmo) ct.writeWhitePhase("white.dat", whitify(density, L, cosmo, supergenerate=supergenerate)) with file("white_params", mode="w") as f: f.write("4\n%lg, %lg, %lg\n" % (cosmo['omega_M_0'], cosmo['omega_lambda_0'], 100*cosmo['h'])) f.write("%lg\n%lg\n-%lg\n0,0\n" % (cosmo['omega_B_0'],cosmo['ns'],cosmo['SIGMA8'])) f.write("-%lg\n1\n0\n\n\n\n\n" % L) f.write("2\n\n0\nwhite.dat\n0\npadding_white.dat\n")